Introduction

This portfolio explores the albums Vulfpeck, their members and collaborators. Vulfpeck’s Joe Dart is praised as one of the best bassists to emerge in recent years, but that’s not the only reason for this choice. Firstly, Vulfpeck is known for their one-take style of recording and off-beat musical style. Secondly, the band has a few closely associated members that appear on a large section of their music. Finally, the band’s members have their own solo projects which often still feature each other. This allows us to compare the albums of these artists and see what makes their music unique. This is the main goal of the analysis in this corpus.

The natural groups in the corpus are as follows. Vulfpeck, Theo Katzmann, Woody Goss, Cory Wong, The Fearless Flyers and Nate Smith. Each of these categories consists of at least 3 albums and almost all albums feature the other artists. Though they share a band there are some clear differences in genre, Vulfpeck is primarily funk, Theo Katzman is more slow love songs, Cory Wong has the danceability of pop music, Woody Goss is minimalistic and has a more straight feel, The Fearless Flyers is extremely high energy and fast paced and Nate Smith’s solo work is odd-timed and syncopated heavy drumming.

It will be interesting to find out what makes each artists music truly theirs, even though they are so closely related and collaborative.

Below you will find a short description of each artists and a typical song of theirs.

The Dataset

What about these typical songs? A closer look at some track-level features

Here we see the typical songs for each artist as mentioned earlier depicted by four track-level features. Some of the things I noticed myself are also representedin the graph.

The Fearless Flyers have the highest energy by far, while the love songs of Theo Katzman and the minimalist funk of Vulfpeck get low scores.

For Theo Katzmans song the valence is also the lowest, which often corresponds to sad songs, much like the melancholy often felt when listening to love songs.

The Dancability feature then seems odd in combination with the others. I would have expected Cory Wong to be the highest by far in this category.

Cory Wong scores high in Valence however, perhaps it was my personal correlation between dancing and happiness, which is often said to be represented by a combination of valence and energy in music.

Finally, Nate Smith scores lowest in Liveness. The track used is a solo drum track, which might explain the low score due to a lack of noise in the recording.

We are certainly gaining some insight into the differences between the artists, but these are just some typical tracks. Lets look at the bigger picture!

Lets see what emotions best describes each artist

Here we see the entire dataset plotted based on valence on the x-axis and energy on the y-axis.

We can already begin to see some larger patterns. Most of the music is happy, and you can see that Fearless Flyers music actually never leaves the happy quadrant. Cory Wongs music also rarely does, but sometimes has a lower valence and ends up in theh angry quadrant.

You can clearly see Theo Katzman, Vulfpeck and Woody Goss are on the lower side of the graph. Overall they have less energy. For Theo Katzmann this is because of the style of music he usually plays, which is half-time smooth love songs. Woody Goss’ and Vulfpeck’s reason is most likely the minimalist production they use. Short notes and interesting rests in the music are something that makes their music uniqie.

Nate Smith is the only one who shows a clear pattern of low valence and high energy. This somewhat understandable, a lot of his music features heavy drum parts which in audio analysis often leads to high and intense peaks. It is difficult to descibe how one would express valence in terms of drums.

Major and Minor modes are often associated with happy and sad, perhaps that is the explanation?

Although the distribution is clearly not random, the modes used don’t seem to correspond with the, perhaps too simple, assumption that we can explain the emotion of the music using just the mode.

We can clearly see Theo Katzman uses almost exclusively Major keys, but still his songs were relatively low energy and valence compared to the other groups, which feature a lot more Minor keys.

Cory Wong, whose music I find the most uplifting of all, has relatively many minor keys.

One thing that is striking is the identical gap in keys between Vulfpeck and the Fearless Flyers for the keys of D# and E. Both these bands feature Joe Dart, perhaps he doesn’t like those keys very much?

Funnily enough, in his solo repetoire Cory Wong also doesn’t prefer d#, but does have a few songs in E.

How does this energy translate into tempo?

Different genres have different tempi, what’s our dataset like?

And how do the categories differ?

What about song structure? Lets go back to the track level and see the similarities.

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When analyzing the structure of the self similarity matrix for timbre(left) and pitch(right) there are some clear structures. The song has a clear pre-chorus and chorus. What is interesting is that the most prominent yellow pitch lines are only for the prechorus, meaning the chorus and verse are very similar in pitch. The main melody is provided by the bass, and indeed the bass part is the same for the chorus and verses, and different for the prechorus.

Here we see the Self Similarity Matrix for “Too hot in LA”

Nate SMith"

Cory Wong

HTheo

Woody

Predicting

Lets put this to the test, we certainly found some differences, but are they distinctive enough?

           Truth
Prediction  Cory Wong Fearless Katzman Nate Vulfpeck Woody
  Cory Wong        29        4       2    7       21     1
  Fearless          1       13       0    4        4     0
  Katzman           2        0      21    3        8     6
  Nate              1        0       0   12        4     0
  Vulfpeck         15        4       5    2       30     3
  Woody             0        1       3    1        7    14

When constructing the KNN network one thing that drastically improved performance is using only the top 10 most influential features. Still there are some interesting observations to be made. Although this confusion matrix gives some insight, it is skewed towards the bigger categories like Vulfpeck and Cory wong, and it only shows numbers, and not a clear measure of performance.

Detailed analysis of performance

# A tibble: 6 x 3
  class     precision recall
  <fct>         <dbl>  <dbl>
1 Cory Wong     0.615  0.667
2 Fearless      0.667  0.545
3 Katzman       0.586  0.548
4 Nate          0.778  0.483
5 Vulfpeck      0.548  0.689
6 Woody         0.444  0.333

As with the KNN network we see in this Random Forest implementation the Fearless Flyers playlist is the most distinctive group in the corpus. .

Importance of features

This shows the most important distinguishing features between the different playlists. Loudness seems to be the key distinguishing feature, followed by instrumentalness and energy. The rest of the top 10 is mainly timbre components, while the key of the song doesn’t seem to contribute all that much to the predictions. .

Clustering

[1] 228

  Vulfpeck Cory Theo Fearless Woody Nate
1       15    7   17        0     9    4
2       23   12    0       14     0    2
3        1    1    0        8     0   10
4       13   17    2        0     4    9
5        4    0    0        0     4    4
6       18   11   12        0     7    0

Here we try clustering in the 6 categories we defined at the beginning of this portfolio. As you can see the groups are certainly not pure. Nate smith seems to concentrate in the upper right, but is also in the lower clusters. I will analyse the exact classifications more precisely in the final portfolio. .

Principal Components

In the previous slide we were shown the 6 clusters our random forest came up with. This is represented in our graph along two dimensions. In machine learning Principal Component Analysis is used to determine a linear combination of the features in our dataset that offer the highest rate of variability. In this circle we see the same two dimensions, but now plotted on top is the contribution of each of our features, the ones we know how to interpret. We can see that acousticness and energy are the biggest contributours for dimension 1, while c01 plays the largest role in dimension two. .